论文标题
优化油轮终端的协调时间表：智能大型时空数据驱动方法 - 第1部分
Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1
论文作者
论文摘要
在这项研究中，提出了一种新颖的协调调度优化方法，以通过减少平均等待时间和周转时间来提高端口效率。提出的方法包括增强的粒子群优化（EPSO）作为内核和增强萤火虫算法（AFA）作为全局最佳搜索。研究了所提出的方法的两种范式方法，这些方法是批处理方法和滚动地平线方法。实验结果表明，提出的方法的两种范式方法都可以有效提高端口效率。平均等待时间可以大大减少86.0％-95.5％，而相对于历史基准，平均周转时间最终可以节省38.2％-42.4％。此外，在3个月的数据集上运行时间的滚动范围方法可以减少到20分钟，而不是在批处理方法上以相应的最大性能减少4小时。
In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of the proposed approach are investigated, which are batch method and rolling horizon method. The experimental results show that both paradigm methods of proposed approach can effectively enhance port efficiency. The average wait time could be significantly reduced by 86.0% - 95.5%, and the average turnaround time could eventually save 38.2% - 42.4% with respect to historical benchmarks. Moreover, the paradigm method of rolling horizon could reduce to 20 mins on running time over 3-month datasets, rather than 4 hrs on batch method at corresponding maximum performance.
